In-Orbit Temperature Prediction of Alpha Magnetic Spectrometer Based on the Neural Network Model

Abstract

The Alpha Magnetic Spectrometer (AMS) is a high-precision particle detector deployed on the International Space Station (ISS) to look for the origin of dark matter, antimatter's existence, and the origin and features of cosmic rays. Analyzing and forecasting the thermal status of electric equipment of AMS is of great significance to ensure that they operate within acceptable temperature limits. In this study, the orbital parameters of the AMS and thermal data of the main radiators are analyzed. Artificial neural network models are established for predicting the temperatures of AMS in orbit under the ISS normal and special operating conditions. The mean squared errors (MSE) of the predictions after the model training show that the established neural network models can accurately predict AMS temperatures. Comparison results between the recorded telemetry data and the predicted temperature obtained from the established neural network models show that the proposed models are precise enough for predicting AMS temperatures with the minimum MSE being 0.006. This work offers a reference for the thermal control of AMS and other spacecraft in orbit.